code: def test_get_best_pred(lower_is_better, expected_best):
      results = pandas.DataFrame(
          {
              "x": numpy.linspace(0, 1, 10),
              "Objective": numpy.linspace(0, 1, 10),
              "Status": ["COMPLETED"] * 10,
          }
      )
      params = [sherpa.Continuous("x", [0, 1])]
      algorithm = GPyOpt(num_initial_data_points=2)
      algorithm.get_suggestion(
          results=results,
          parameters=params,
          lower_is_better=lower_is_better,
      )
      best_params = algorithm.get_best_pred(
          results=results,
          parameters=params,
          lower_is_better=lower_is_better,
      )
      assert best_params["x"] == expected_best
  
imports: import sherpa
  import pandas
  import pytest
  import numpy
  import collections
  import GPyOpt
  from sherpa.algorithms.bayesian_optimization import GPyOpt
  from sherpa.algorithms import Repeat
label_desc: Use image node_id 16 for calling a global function with example usage: test_get_best_pred(lower_is_better, expected_best) without return types